81 lines
		
	
	
		
			2.3 KiB
		
	
	
	
		
			Python
		
	
	
	
	
	
			
		
		
	
	
			81 lines
		
	
	
		
			2.3 KiB
		
	
	
	
		
			Python
		
	
	
	
	
	
'''
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Robust line fitting.
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==================
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Example of using cv2.fitLine function for fitting line 
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to points in presence of outliers.
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Usage
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-----
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fitline.py
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Switch through different M-estimator functions and see, 
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how well the robust functions fit the line even 
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in case of ~50% of outliers.
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Keys
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----
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SPACE - generaty random points
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f     - change distance function
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ESC   - exit
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'''
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import numpy as np
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import cv2
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import itertools as it
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from common import draw_str
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w, h = 512, 256
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def toint(p):
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    return tuple(map(int, p))
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def sample_line(p1, p2, n, noise=0.0):
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    p1 = np.float32(p1)
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    t = np.random.rand(n,1)
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    return p1 + (p2-p1)*t + np.random.normal(size=(n, 2))*noise
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dist_func_names = it.cycle('CV_DIST_L2 CV_DIST_L1 CV_DIST_L12 CV_DIST_FAIR CV_DIST_WELSCH CV_DIST_HUBER'.split())
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cur_func_name = dist_func_names.next()
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def update(_=None):
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    noise = cv2.getTrackbarPos('noise', 'fit line')
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    n = cv2.getTrackbarPos('point n', 'fit line')
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    r = cv2.getTrackbarPos('outlier %', 'fit line') / 100.0
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    outn = int(n*r)
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    p0, p1 = (90, 80), (w-90, h-80)
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    img = np.zeros((h, w, 3), np.uint8)
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    cv2.line(img, toint(p0), toint(p1), (0, 255, 0))
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    if n > 0:
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        line_points = sample_line(p0, p1, n-outn, noise)
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        outliers = np.random.rand(outn, 2) * (w, h)
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        points = np.vstack([line_points, outliers])
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        for p in line_points:
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            cv2.circle(img, toint(p), 2, (255, 255, 255), -1)
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        for p in outliers:
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            cv2.circle(img, toint(p), 2, (64, 64, 255), -1)
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        func = getattr(cv2.cv, cur_func_name)
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        vx, vy, cx, cy = cv2.fitLine(np.float32(points), func, 0, 0.01, 0.01)
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        cv2.line(img, (int(cx-vx*w), int(cy-vy*w)), (int(cx+vx*w), int(cy+vy*w)), (0, 0, 255))
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    draw_str(img, (20, 20), cur_func_name)
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    cv2.imshow('fit line', img)
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if __name__ == '__main__':
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    print __doc__
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    cv2.namedWindow('fit line')
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    cv2.createTrackbar('noise', 'fit line', 3, 50, update)
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    cv2.createTrackbar('point n', 'fit line', 100, 500, update)
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    cv2.createTrackbar('outlier %', 'fit line', 30, 100, update)
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    while True:
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        update()
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        ch = cv2.waitKey(0)
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        if ch == ord('f'):
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            cur_func_name = dist_func_names.next()
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        if ch == 27:
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            break
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